Scalability and stability of multi‑agent reinforcement learning for multi‑robot interaction

Establish scalable and stable multi‑agent reinforcement learning algorithms and analysis for multi‑robot interaction that can handle large numbers of agents, guarantee convergence or robustness, and enable reliable real‑world deployment.

Background

Multi‑robot interaction problems rapidly increase in complexity as team size grows, and current MARL methods often struggle with scalability and stability, limiting real‑world applicability.

The survey identifies the scalability and stability of MARL as explicit open questions that hinder broader use in multi‑robot coordination, collision avoidance, and cooperative tasks.